Abstract
Exploring a group of influential spreaders to acquire maximum influence has become an emerging area of research in complex network analysis. The main challenge of this research is to identify the group of important nodes that are scattered broadly, such that the propagation ability of information is maximum to a network. Researchers proposed many centrality-based approaches with certain limitations to identify the influential nodes (spreaders) considering different properties of the networks. To find a group of spreaders, the VoteRank (a voting mechanism) based method produces effective results with low time complexity, wherein each iteration, the node votes for its neighbors by its voting capability, and the node obtaining the maximum vote score is identified as an influential spreader. The major loophole of existing VoteRank methods is measuring the voting capability based on the degree, k-shell index, or contribution of neighbors methods, which does not efficiently identify the spreaders from the diverse regions based on their spreading ability. In this paper, we propose a novel Community-based VoteRank method (CVoteRank) to identify a group of influential spreaders from diverse network regions by which the diffusion process is enhanced. Firstly, we measure every node's spreading ability based on intra- and inter-connectivity structure in a community, which signifies the local and global importance of the node. To identify the seed nodes, we assign the spreading ability to that node's voting capability and iteratively calculate the voting score of anode based on its neighboring voting capability and its spreading ability. Then, the node acquiring the maximum voting score is identified as the influential spreader in each iteration. Finally, to solve the problem of influence overlapping, CVoteRank reduces the voting capability of the neighboring nodes of the identified spreader. The efficiency of CVoteRank is evaluated and compared with the different state-of-the-art methods on twelve real networks. Utilizing the stochastic susceptible-infected-recovered epidemic method, we calculate the infected scale, final infected scale, and the average shortest path length among the identified spreaders. The experimental results show that CVoteRank identifies the most efficient spreaders with the highest spreading ability within a short period and the maximum reachability, and the identified spreaders are situated at diverse portions of the networks.

Abstract
Developed within the Iliad Digital Twin of the Ocean (DTO) project, Coastal Crete provides advanced marine forecasting for oil spill detection and response. The system integrates satellite data, in-situ observations, and machine learning to predict oil spill trajectories and minimize environmental impacts. Using a multi-model approach, it combines WRF-DA, NEMO, and WAVEWATCH III models for high-resolution forecasts. Making use of Sentinel-1 SAR imagery, a deep learning approach was developed for near-real-time oil spill detection. The methodology is based on a U-net Neural Network, which is compared with the statistical methodology based on pythons' SNAPpy library. The operational forecasting system employs MEDSLIK-II for oil spill transport modeling and visualization via the GeoMachine platform, ensuring rapid decision-making for marine safety and environmental protection.

Abstract
This study examines the social factors influencing the acceptance of autonomous buses, with a focus on per-ceived benefits, safety, and comfort. It also explores whether these factors differ among residents of cities with varying sizes and urban mobility solutions. A survey was conducted in three Polish cities, collecting data from 1,160 respondents. Structural Equation Modelling (SEM) was used to analyse relationships between perceived benefits, safety, comfort, and future intentions to use autonomous buses. Results indicate that safety and comfort positively influence future intentions to use autonomous buses. However, the effect of perceived benefits varies across cities, suggesting that urban mobility conditions shape public acceptance. The study focuses on Polish cities, which may limit generalizability. Future research should examine other geo-graphical contexts. Findings provide insights for policymakers and manufacturers on enhancing public trust and promoting autonomous bus adoption. Improving public awareness and addressing safety concerns may increase societal acceptance of autonomous mobility. The study uniquely assesses how city characteristics influence social acceptance of autonomous buses.

Abstract
Background: During their training, nurses must develop interprofessional collaboration skills, which are essential in clinical settings. Aim: This study aims to describe the development and testing stages of a virtual escape room, named Lock-down Treatment, to enhance interprofessional collaboration. Methods: The User-Centered Design methodology was used, involving users from requirement gathering to iterative prototyping. Requirements were established through interviews with 6 healthcare professionals, and a prototype was developed and tested for final assessment. Results: The results identified key areas for improvement, particularly in terms of timing and support during the game and demonstrated the effectiveness of the escape room in promoting interdisciplinary collaboration. This study proves that tools like escape rooms can significantly enrich nursing education. Conclusion: It is essential to integrate innovative methods into interprofessional training, making it more engaging and interactive. However, it is crucial that such tools are meticulously planned and validated to ensure their suitability through a rigorous validation process. Future research should evaluate the 'Lockdown Treatment' to assess its long-term effectiveness and applicability in clinical practice and patient outcomes. (c) 2025 The Authors. Published by Elsevier Inc. on behalf of Organization for Associate Degree Nursing. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Abstract
Small to medium-sized shipyards play a crucial role in the European naval industry. However, the globalization of technology has increased competition, posing significant challenges to shipyards, particularly in domestic markets for short sea, work, and inland vessels. Many shipyard operations still rely on manual, labor-intensive tasks performed by highly skilled operators. In response, the adoption of new tools is essential to enhance efficiency and competitiveness. This paper presents a methodology for developing a human-centric portfolio of advanced technologies tailored for shipyard environments, covering processes such as shipbuilding, retrofitting, outfitting, and maintenance. The proposed technological solutions, which have achieved high technology readiness levels, include 3D modeling and digitalization, robotics, augmented and virtual reality, and occupational exoskeletons. Key findings from real-scale demonstrations are discussed, along with major development and implementation challenges. Finally, best practices and recommendations are provided to support both technology developers seeking fully tested tools and end users aiming for seamless adoption.

Abstract
The application of digital twin technology to the ocean is often referred to as Digital Twins of the Ocean (DTO). One notable initiative funded under Horizon Europe programs - Green Deal is the ILIAD - Digital Twin of the Ocean project. One of the objectives of ILIAD is to establish interoperable, data-intensive, and cost-effective DTO pilots. This paper focuses on one such pilot dedicated to environmental monitoring and water quality assessment associated with the OceanLab infrastructure in the Trondheim Fjord, Norway. This paper outlines the architecture and concept of the pilot while providing detailed insights into its application for various what-if scenarios. The scenario presented in this paper is a case study that analyzes the impact of a hypothetical oil spill at the Trondheim terminal. It focuses on the spread of surface oil over a 30-hour period using various pilot modules. The paper also discusses the potential replication of this study in another geographical location.